Laboratory for Human Craniofacial and Skeletal Identification (HuCS-ID Lab), School of Biomedical Sciences, The University of Queensland, Brisbane, 4072, Australia.
Int J Legal Med. 2024 Mar;138(2):519-535. doi: 10.1007/s00414-023-03087-x. Epub 2023 Oct 7.
This year (2023) marks 140 years since the first publication of a facial soft tissue thickness (FSTT) study. Since 1883, a total of 139 studies have been published, collectively tallying > 220,000 tissue thickness measurements of > 19,500 adults. In just the last 5-years, 33 FSTT studies have been conducted. Herein, we add these data (plus an additional 20 studies) to the 2018 T-Table to provide an update of > 81,000 new datapoints to the global tallied facial soft tissue depths table. In contrast to the original 2008 T-Table, some notable changes are as follows: increased FSTTs by 3 mm at infra second molar (ecm-iM'), 2.5 mm at gonion (go-go'), 2 mm at mid-ramus (mr-mr'), and 1.5 mm at zygion (zy-zy'). Rolling grand means indicate that stable values have been attained for all nine median FSTT landmarks, while six out of nine bilateral landmarks continue to show ongoing fluctuations, indicating further data collection at these landmarks holds value. When used as point estimators for individuals with known values across 24 landmarks (i.e., C-Table data), the updated grand means produce slightly less estimation error than the 2018 T-Table means (3.5 mm versus 3.6 mm, respectively). Future efforts to produce less noisy datasets (i.e., reduce measurement and sampling errors as much as possible between studies) would be useful.
今年(2023 年)标志着第一篇面部软组织厚度(FSTT)研究发表 140 周年。自 1883 年以来,共发表了 139 项研究,总计有超过 22 万名成年人的软组织厚度测量数据超过 19500 个。就在过去的 5 年里,已经进行了 33 项 FSTT 研究。在此,我们将这些数据(加上另外 20 项研究)添加到 2018 年的 T 表中,为全球面部软组织深度统计表提供了超过 81000 个新数据点的更新。与原始的 2008 年 T 表相比,以下是一些值得注意的变化:在下颌第二磨牙(ecm-iM')处的 FSTT 增加了 3mm,在髁突(go-go')处增加了 2.5mm,在下颌支中部(mr-mr')处增加了 2mm,在颧突(zy-zy')处增加了 1.5mm。滚动平均值表明,所有九个中间软组织厚度标志点都已达到稳定值,而九个双侧标志点中有六个仍在继续波动,表明在这些标志点进一步收集数据仍具有价值。当用作具有已知 24 个标志点值的个体的点估计器(即 C 表数据)时,更新后的平均值产生的估计误差略小于 2018 年 T 表平均值(分别为 3.5mm 和 3.6mm)。未来的努力方向是生成更少噪声的数据集(即尽可能减少研究之间的测量和采样误差),这将是有用的。